In order to successfully reproduce, plants must sense changes in their environment and flower at the correct time. Many plants utilize day length and vernalization, a mechanism for verifying that winter has occurred, to determine when to flower. Our study used available temperature and day length data from different climates to provide a general understanding how this information processing of environmental signals could have evolved in plants. For climates where temperature fluctuation correlations decayed exponentially, a simple stochastic model characterizing vernalization was able to reconstruct the switch-like behavior of the core flowering regulatory genes. For these and other climates, artificial neural networks were used to predict flowering gene expression patterns. For temperate plants, long-term cold temperature and short-term day length measurements were sufficient to produce robust flowering time decisions from the neural networks. Additionally, evolutionary simulations on neural networks confirmed that the combined signal of temperature and day length achieved the highest fitness relative to neural networks with access to only one of those inputs. We suggest that winter temperature memory is a well-adapted strategy for plants’ detection of seasonal changes, and absolute day length is useful for the subsequent triggering of flowering.

为了成功繁殖，植物必须在正确的时间感知周围环境的变化和花朵。许多植物利用天长和春化来确定何时开花，这是一种用于确认冬天已经发生的机制。我们的研究使用了来自不同气候的可用温度和日长数据，以大致了解这种环境信号的信息处理如何在植物中进化。对于温度波动相关性呈指数衰减的气候，表征春化的简单随机模型能够重建核心开花调控基因的开关状行为。对于这些和其他气候，人工神经网络被用来预测开花基因的表达模式。对于温带植物，长期的低温和短期的日长测量足以从神经网络中得出可靠的开花时间决定。此外，对神经网络的进化仿真证实，相对于只能访问这些输入之一的神经网络，温度和日长的组合信号达到了最高的适用性。我们建议冬季温度记忆是一种适合植物检测季节变化的策略，绝对日长可用于随后触发开花。神经网络的进化模拟证实，温度和日长的组合信号相对于只能访问这些输入之一的神经网络而言，具有最高的适用性。我们建议冬季温度记忆是一种适合植物检测季节变化的策略，绝对日长可用于随后触发开花。神经网络的进化模拟证实，温度和日长的组合信号相对于只能访问这些输入之一的神经网络而言，具有最高的适用性。我们建议冬季温度记忆是一种适合植物检测季节变化的策略，绝对日长可用于随后触发开花。